Crowd sourced digital content processing

ABSTRACT

Through use of crowd sourced information, media files may be transformed into or used to create product files that are derived from the media files. A product file may be generated using feedback from the crowd sourced information received from an electronic device. The crowd sourced information may indicate one or more portions of the media file to exclude from the product file to create a consolidated product file. In some embodiments, the crowd sourced information may indicate supplemental material and/or portions of other media files that may be included in the product file.

BACKGROUND

Media player devices enable users to consume digital content in the formof media files (including streamed media) at the convenience of theusers. With the proliferation of media files and Internet-based portalsfor uploading, downloading, and streaming media files, users may obtainnot only professionally produced media files, but also media files thatare produced by private individuals. In some instances, a user maydesire to consume particular portions of a media file that are relevantto a specific subject. In other instances, a user may desire to view orlisten to an abridged version of the media file without viewing orlistening to the entire media file. In such instances, the user may beforced to hunt for pertinent sections of a media file using userinterface control such as fast forward, skip, rewind, and replay.Accordingly, a user may spend a considerable amount of time and effortto find such pertinent sections, which may cause frustration andinconvenience for the user.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures. In the figures, the left-most digit(s) of a reference numberidentifies the figure in which the reference number first appears. Thesame reference numbers in different figures indicate similar oridentical items.

FIG. 1 is a schematic diagram of an illustrative architecture forimplementing various embodiments of crowd sourced processing of mediafiles that include digital content.

FIG. 2 is a schematic diagram of illustrative components that implementcrowd sourced processing of media files that include digital content.

FIG. 3 is a block diagram that illustrates example user interfaces forcrowd sourcing the creation of modified media files from existing mediafiles that include digital content.

FIG. 4 is a block diagram that illustrates an example user interface forcrowd sourcing the creation of compilation files from exiting mediafiles.

FIG. 5 is a flow diagram of an illustrative process for crowd sourcingmodified media files for multiple users and distributing the modifiedmedia files.

FIG. 6 is a flow diagram of an illustrative process for creating acompilation file that is generated based on crowd sourced ratings ofsections in a media file.

FIG. 7 is a flow diagram of an illustrative process for creating acompilation file that is generated based on crowd source ratings ofsections in multiple media files.

FIG. 8 is a flow diagram of an illustrative process for customizing acompilation file based on user characteristics and supplementalmaterial.

FIG. 9 is a flow diagram of an illustrative process for identifying amedia file based on a comparison of the media file to a known mediafile.

FIG. 10 is a flow diagram of an illustrative process for providing anincentive to a user for contributing to the crowd sourced processing ofmedia files.

FIG. 11 is a flow diagram of an illustrative process for crowd sourcingmedia files related an event to create a media presentation for theevent.

DETAILED DESCRIPTION

Overview

The disclosure is directed to architectures and techniques for crowdsourcing the processing of media files (including streaming media) thatinclude digital content to create modified media files and compilationsfiles. As described herein, media files may include audio files, videofiles, and/or multimedia files. The crowd sourced processing of a mediafile may include the condensing of a media file into a condensed mediafile. The condensed media file may be a media file that pertains to aparticular subject or a media file that highlights particular portionsof the original media file. For example, the media file may be a summaryof the original media file. In this way, a user that receives thecondensed media file may obtain the relevant information while spendingless time consuming a media file.

The crowd sourced processing of media files may also include thestitching of sections from multiple media files into a compilation file,such that the compilation file incorporates selected sections of themultiple media files. For example, the newly created compilation filemay be tailored according to the explicit or the implicit usercharacteristics of a recipient user, such that the recipient user findsthe compilation file to be particularly relevant to the taste or needsof the recipient user.

The crowd sourced processing of media files may further include thesupplementation of the media file or a compilation file with additionalrelevant content or explanatory material. Such supplementation of amedia file may serve to provide a recipient user of the media file witha greater understanding or appreciation of the content described in themedia file. Additionally, the crowd sourced processing of media filesmay also include the stitching of multiple media files that are relevantto an event into a media presentation for the event. The mediapresentation may enable a recipient user to gain insight and perspectiveinto the event that are otherwise not apparent without the crowd sourcedcontent and the crowd sourced organization of such content.

In various instances, users that contributed to the crowd sourcedprocessing of the media files may receive monetary and/or non-monetaryincentives. The value of an incentive that is provided to a contributinguser may be dependent on the peer perceived quality or usefulness of thecontribution from the contributing user. The non-monetary incentives maybe in the form of recognition, such as an increase in user rating on anumerical scale or acknowledgement of contribution in creatingparticular modified media files or compilation files. The monetaryincentive may be a flat fee monetary award, or a percentage monetaryaward that is based on proceeds from the sale of a modified media fileor a compilation file. Other monetary awards may include a giftcertificate, a coupon, store credit, or other forms of credits that maybe used to offset the cost of purchasing goods or services.

The use of crowd sourced processing of media files may yield modifiedmedia files and compilation files that provide users with the ability toacquire pertinent content in reduced amounts of time. Such reduction inamounts of time may enable users to more efficiently gain knowledge,insight, and/or enjoyment from the media files.

Illustrative System Architecture

FIG. 1 is a schematic diagram of an illustrative environment 100 forimplementing various embodiments of crowd sourced processing of mediafiles that include digital content. The computing environment 100 mayinclude a server 102 and a plurality of electronic devices 104(1)-104(N)that are communicatively connected by a network 106. In variousembodiments, the electronic devices may include a desktop computer104(1), a portable computer 104(2), and an electronic book reader device(or, an “eBook reader device”) 104(N). Each of the electronic devices104(1)-104(N) may have software and hardware components that enable thedisplay of video and/or the playback of audio content in various files.However, the recipient devices 104(1)-104(N) are merely examples, andother electronic devices that are equipped with network communicationcomponents, data processing components, electronic displays fordisplaying data, and/or audio output capabilities may be employed.

The network 106 may be a local area network (“LAN”), a larger networksuch as a wide area network (“WAN”), or a collection of networks, suchas the Internet. Protocols for network communication, such as TCP/IP,may be used to implement the network 106. Although embodiments aredescribed herein as using a network such as the Internet, otherdistribution techniques may be implemented that transmit information viamemory cards, flash memory, or other portable memory devices.

A crowd source media engine 108 on the server(s) 102 may use the network106 to present media files 112 to users at the electronic devices104(1)-104(N). Each of the electronic devices 104(1)-104(N) may includea web browser that enables a corresponding user to navigate to a webpage presented by the crowd source media engine 108. Each web page maypresent one or more media file for a user to play back. As describedherein, media files may include audio files, video files, and/ormultimedia files. Accordingly, the play back of a multimedia file mayinvolve the playing of audio tracks and/or video footages.

The web pages may further include user interface controls that enablethe users to provide user contributions 114(1)-114(N) with respect tothe media files 112. In some instances, the user contribution 114(1)from the electronic device 104(1) may be a user modified version of amedia file. For example, the user modified version of an audio file maybe a condensed version of the audio file in which irrelevantintroductions and/or long pauses between dialogues have been removedusing an application user interface on the electronic device 104(1). Inother instances, the user contribution 114(2) may be the division of amedia file into multiple sections using an application user interface onthe electronic device 104(2), such that the sections may be furtheranalyzed by other users. For example, the user of the electronic device104(2) may use an application user interface to divide a multimedia filethat is a lecture into sections according to lecture topics. In thisway, additional users may be further crowd sourced into rating thepresentation quality of the sections. Based on the ratings assigned tothe sections, the crowd source media engine 108 may generate a modifiedmultimedia file that includes sections of the multimedia file withratings that meet a minimal rating threshold. For example, themultimedia file may include sections for advertisements. Since theratings for such advertisement sections failed to meet a minimal ratingthreshold after a predetermined number of votes have been cast by theadditional users, the crowd source media engine 108 may remove suchadvertisement sections to generate a modified multimedia file that doesnot include these advertisements.

In additional instances, the user contribution 114(N) may be userratings for sections of multiple related media files. For example,multiple related media files may be multimedia files of a particularlecture that is delivered by a speaker at various times and placesaround the country. Accordingly, the user contribution 114(N) may beratings of the sections of the multiple related media files. In such anexample, at least some of the sections in one lecture may havecorresponding sections in another lecture, as each lecture may cover thesame topics and the sections correspond to such topics. As such, thecrowd source media engine 108 may combine the user contribution 114(N)with ratings of the sections from other users. Based on the collectiveratings from the multiple users, the crowd source media engine 108 maygenerate a compilation file that includes the highest rated section fromeach set of corresponding sections.

In further instances, the user contribution from a user may be thefiltering or the organization of media files that are related to anevent. For example, the media files may be submitted video footages of acar accident that occurred at a particular intersection. The submittedvideo footages may be taken from different perspectives around theintersection and/or at different times. Accordingly, the usercontribution from the user may include the temporal tagging of the videofootages in chronological order. Alternatively or concurrently, the usercontribution from the user may include the geo-tagging of the videofootages according to video shot locations and/or video shotorientations. In this way, the user contribution data submitted by theuser may be combined with other data by the crowd source media engine108. Accordingly, when a sufficient number of geo tags and/or temporaltags submitted are in agreement, the crowd source media engine 108 mayuse the resulting location, orientation, and/or temporal data for thevideo footages to generate a stitched media representation of the event.In various embodiments, the generation of the media representation mayinclude temporally overlapping and/or spatially overlapping data from atleast some of the multiple media files. The overlapped data may includevisual data and/or audio data. For example, the stitched mediarepresentation may simultaneously display multiple views of actionsequences from the event, such that cause and effect between differentoccurrences in the event may be understood. Other crowd sourced usercontributions from the users may include language translations ofspecific speech content in the media files, and/or explanatory orsupplemental content for topics covered in the media files.

The crowd source media engine 108 may distribute product files 116 to aplurality of recipient devices 118(1)-118(N) for consumption by users.The product files 116 may include modified media files, compilationfiles, stitched media presentation, and/or other files that aregenerated by the crowd source media engine 108. In various embodiments,the recipient devices may include a mobile phone 118(1), a portablecomputer 118(2), a tablet computer 118(3), and an electronic book readerdevice (or, an “eBook reader device”) 118(N).

Each of the recipient devices 118(1)-118(N) may have software andhardware components that enable the display of video and/or the playbackof audio from the product files 116. However, the recipient devices118(1)-118(N) are merely examples, and other electronic devices that areequipped with network communication components, data processingcomponents, electronic displays for displaying data, and/or audio outputcapabilities may be employed. For example, the recipient devices118(1)-118(N) may include desktop computers, servers, televisions,set-top boxes, digital media receivers, and other non-portable types ofelectronic devices.

In some embodiments, the product files 116 that are generated by thecrowd source media engine 108 may be tailored to implicit or explicituser characteristics of the users that operate the recipient devices118(1)-118(N). For example, a media file in the form of an audio filemay include sections that contain English dialogue and sections thatcontain French dialogue. However, since the user of the recipient device118(1) explicitly indicated that the user does not understand French, aproduct file that is generated by the crowd source media engine 108 fromthe media file may include only one or more of the sections that containEnglish dialogue. In another example, a media file may include two typesof sections. One type of sections includes audio discussions of a firsttopic (e.g., fashion) and the other type of sections includes audiodiscussion of a second topic (e.g., electronics). In such an example,the crowd source media engine 108 may determine which section to includein the product from the implicit characteristics of the user. The crowdsource media engine 108 may infer the implicit characteristics of theuser from the purchasing habits of the user at one or more onlinemerchants and/or a prior product file download history of the user. Inan instance in which the implicit user characteristics indicate that theuser is interested in electronics but not fashion, the crowd sourcemedia engine 108 may generate a product file from the media file thatincludes one or more sections that pertain to electronics, but nosection that pertains to fashion.

Example Server Modules

FIG. 2 is a schematic diagram of illustrative components of a crowdsource media engine 108 that implement crowd sourced processing of mediafiles. The crowd source media engine 108 may be implemented by the oneor more servers 102. The one or more servers 102 may be equipped withnetwork interfaces 202, processor(s) 204, and computer readable media206. The network interfaces 202 may include wireless and/or wirelesscommunication interface components that enable the servers 102 totransmit and receive data via a network. In various embodiments, thewireless interface component may include, but is not limited tocellular, Wi-Fi, Ultra-wideband (UWB), Bluetooth, satellitetransmissions, and/or so forth. The wired interface component mayinclude a direct input/output (I/O) interface, such as an Ethernetinterface, a serial interface, a Universal Serial Bus (USB) interface,and/or so forth.

The computer readable media 206 may include non-transitorycomputer-readable storage media, which may include hard drives, floppydiskettes, optical disks, CD-ROMs, DVDs, read-only memories (ROMs),random access memories (RAMs), EPROMs, EEPROMs, flash memory, magneticor optical cards, solid-state memory devices, or other types of storagemedia suitable for storing electronic instructions. In addition, in someembodiments the computer-readable media may include a transitorycomputer-readable signal (in compressed or uncompressed form). Examplesof computer-readable signals, whether modulated using a carrier or not,include, but are not limited to, signals that a computer system hostingor running a computer program can be configured to access, includingsignals downloaded through the Internet or other networks.

The crowd source media engine 108 may include a media collection module208, a media identification module 210, a media division module 212,compilation module 214, a distribution module 216, a media rating module218, a user rating module 220, an incentive module 222, and anadministration module 224. The computer readable media 206 may alsoimplement a data store 226.

The media collection module 208 may receive media files that aresubmitted from a plurality of electronic devices, such as the electronicdevices 104(1)-104(N). The media collection module 208 may store thereceived media files in the data store 226. The submitted media filesmay include associated metadata that identify the media files. Themetadata information for a media file may include an Internet Protocol(IP) address of the electronic device that transmitted the media file,the name and version of the web browser that is used to submit the mediafile, and/or other hypertext transfer protocol (HTTP) header informationrelated to the media file.

In some embodiments, the media collection module 208 may extractembedded identification information from the media file. The embeddedidentification information for a media file may include embedded fileidentification information (e.g., a globally unique identifier),embedded global positioning system (GPS) coordinate information,embedded time and date information, embedded orientation informationfrom onboard sensors (e.g., compass, gyroscope, accelerometer, etc.),and/or so forth.

The media collection module 208 may collect other information for amedia file that is submitted by the user, such as information related todate, time, location, and persons involved with the media file,including identification information (e.g., name and contactinformation) of the uploading user. For example, when the user uploads amedia file that is a recording of a presentation, the user may includeinformation such as a name of the presenter, name of the person thatrecorded the presentation, a date and/or a time of the presentation, astreet address of the presentation, a duration of the presentation,and/or so forth. The media collection module 208 may store theinformation and metadata that are acquired from the various sources inthe data store 226.

The media identification module 210 may identify the media files thatare received by the media collection module 208. The identification of amedia file may be performed based on the metadata and/or otherinformation that are obtained by the media collection module 208. Once amedia file is identified, the media identification module 210 maygenerate a corresponding identification entry for the media file. Invarious embodiments, the identification entry may include an internalGUID that is used by the crowd source media engine, as well as otheridentification information. Such identification information may includedate information, time information, location information, orientationinformation, submitter information, submitter information, and/or soforth.

In some instances, a submitted media file may include little or noassociated information that helps to identify the media file. In suchinstances, the media identification module 210 may use machine learningto identify the media file. In at least one embodiment, the mediaidentification module 210 may use a speech-to-text algorithm to extractwords from the audio track of the media file. Subsequently, the mediaidentification module 210 may tokenize words into part of speech tokens,e.g., nouns, verbs, adjectives, etc. The media identification module 210may compare the tokens that resulted from the unknown media file withtokens that are extracted from other known media files. Accordingly,when a token match rate between the tokens from the unknown media fileand the tokens from a known media file reaches a predetermined matchthreshold, the media identification module 210 may assign theidentification information from the known media file to the unknownmedia file. In this way, the media identification module 210 mayidentify media files that are submitted without associatedidentification information.

The match threshold that is used for the media file may be dependent onthe category of the media file. For example, the category may be asubject matter category, an importance category, a source category, orso forth. The media file may be classified into an important categorybased on a level of importance that is assigned to the media file (e.g.,importance levels 1-5). The media file may be assigned to a sourcecategory based on the origin of the media file. For example, media filesproduced by professionals may be placed in a category with a higher orlower match threshold than a category for media files uploaded byamateurs. The match threshold for the categories may be stored as a listin the data store 226 for access by the media identification module 210.

In additional embodiments, the media identification module 210 may alsotake into account one or more additional pieces of information that areassociated with an unknown media file, in conjunction with the tokenmatch threshold, to assign identification information to the unknownmedia file. Such additional information may be embedded or collectedmetadata. For example, the unknown media file may include GPScoordinates of the location at which the media file was created. Thus,the media identification module 210 may assign identificationinformation when the token matching reaches a matching threshold and theGPS coordinates associated with the unknown media file is within apredetermined proximity of the GPS coordinates associated with the knownmedia file. In another example, a token matching rate may be used withthe submission IP addresses of the known and unknown media files (e.g.,IP addresses indicates that the two media files are submitted from aparticular geographic region) to determine that identificationinformation is to be assigned to the unknown media file.

The machine learning used by the media identification module 210 mayalternatively or concurrently involved the use of an image recognitionalgorithm. For example, an image recognition algorithm may be used tocompare the objects that are depicted in the shots from an unknown mediafile and a known media file. The objects may be faces of people, imagesof buildings, and/or other features that are captured in the mediafiles. Accordingly, when a sufficient number of shots from both theunknown media file and the known media file are determined to depict thesame feature, the media identification module 210 may assign theidentification information from the known media file to the unknownmedia file. However, in other instances, image correlation may be usedin conjunction with speech correlation and/or correlation based on oneor more additional pieces of information to identify unknown media filesfor the purpose of assigning identification information to the unknownmedia files.

In other instances, the media identification module 210 may leveragecrowd sourcing to identify unknown media files. In such instances, themedia identification module 210 may present an unknown media file tousers at multiple electronic devices. For example, the mediaidentification module 210 may post the unknown media file on a web pagethat is accessible to the users of the multiple electronic devices104(1)-104(N). Accordingly, when a threshold percentage of a totalnumber of users (e.g., 30 out of 40 users) identify the unknown mediafile as being a particular media file, the media identification module210 may associate the corresponding identification information with theunknown media file. In some instances, the media identification module210 may leverage crowd sourcing to identify unknown media files that arenot identifiable via machine learning and/or from embedded data.

Alternatively, the media identification module 210 may also apply crowdsourcing for media file event tagging. For example, the mediaidentification module 210 may present media files so that users mayprovide temporal tagging or geo-tagging information for the media files.In some instances, once the media identification module 210 has assignedidentification information to a media file, the media identificationmodule 210 may group the identified media file with the known mediafile.

The media identification module 210 may serve to group similar mediafiles into a group. In various embodiments, the media identificationmodule 210 may use the identification information of the media files tocorrelate the media files. For example, if the metadata of two mediafiles indicate that the media files are multimedia recordings of aparticular presentation that is given by a lecturer, the mediaidentification module 210 may group them together as being related. Inadditional embodiments, the media identification module 210 may use animage recognition algorithm to group media files. For example, the imagerecognition algorithm may indicate that two media files depict one ormore common objects and/or persons. As such, when the two media filesdepict the one or more common objects and/or persons for a predeterminedamount of time (e.g., a fixed time interval or a percentage of theoverall temporal length of the media files), the media identificationmodule 210 may group the two media files into a corresponding group. Inalternative embodiments, the media identification module 210 may causethe multiple media files to be displayed on the electronic devices ofmultiple users. In this way, the media identification module 210 mayleverage crowd sourcing to group the media files.

The media division module 212 may divide media files into sections basedon machine learning or crowd sourcing. The division of a media fileusing machine learning may employ an audio discontinuity recognitionalgorithm or a visual discontinuity recognition algorithm. For example,the audio discontinuity recognition algorithm may monitor the spokenwords that are in a media file in the form of an audio recording.Accordingly, when the audio discontinuity algorithm detects that thespoken words transitions from being related to a first topic to beingrelated to a second topic, the media division module 212 may divide themedia file into sections at that transition point. In other instances,the audio discontinuity algorithm may alternatively or concurrently useother audio clues to divide a media file into sections. Such audio cluesmay include changes in the persons speaking that are detected throughvoice recognition, the absence or presence of background noise (e.g.,static, music, ambient noise, etc.), changes in the audio channel thatis picking up audio, and/or so forth.

The media division module 212 may use the visual discontinuityrecognition algorithm to detect scene transitions in a media file thatinclude video. The scene transitions may be detected through changes inlighting, changes in background objects, changes in foreground objects,change in field of view, and/or so forth. Accordingly, when the visualdiscontinuity recognition algorithm detects a scene transition, themedia division module 212 may divide the media file into sections at thescene transition.

In other embodiments, the media division module 212 may leverage crowdsourcing to divide a media file into sections. In such embodiments, themedia division module 212 may present the media file to users atmultiple electronic devices. For example, the media division module 212may post the unknown media file on a web page that is accessible to theusers of the multiple devices. Accordingly, the media division module212 may divide a media file into sections at a particular point when athreshold percentage of a total number of users (e.g., 30 out of 40users) identifies the particular point as a transition point. In thisway, the media division module 212 may leverage crowd sourcing to splita media file into multiple sections. In some embodiments, the mediadivision module 212 may leverage the metadata that are embedded within amedia file to divide a media file into section. For example, themetadata may indicate the time length of each section in a media fileand/or the start time and end time of each section. In this way, themedia division module 212 may break the media file into sectionsaccording to the embedded metadata.

In additional embodiments, the media division module 212 may alsocorrelate sections from multiple media files into sets of correspondingsections. Each set of corresponding sections may have one or moresimilarity characteristics in common. For example, a set ofcorresponding sections may pertain to a common topic, relate to a commonevent, and/or depict the same objects or persons. In such embodiments,the media division module 212 may use various techniques to determinesimilarities between sections for grouping the sections. For instance,the media division module 212 may rely on the metadata that are embeddedin the multiple media files when the metadata indicates common traits ofthe sections. In other instances the media division module 212 may usethe comparison of tokens that are generated from the spoken words in thesections of the media files. Thus, similar to the media identificationmodule 210, the media division module 212 may determine that twosections correspond to each other when a token matching rate between thesections meets a matching threshold. In additional instances, the mediadivision module 212 may use an image recognition algorithm to groupsections into sets of corresponding sections. For example, the imagerecognition algorithm may indicate that two sections from differentmedia files depict one or more common objects and/or persons. As such,when the two sections depict the one or more common objects and/orpersons for a predetermined amount of time (e.g., a fixed time intervalor a percentage of the overall temporal length of the sections), themedia division module 212 may group the two sections into acorresponding set. In alternative embodiments, the media division module212 may cause the sections of the multiple media files to be displayedon the electronic devices of multiple users. In this way, the mediadivision module 212 may leverage crowd sourcing to group sections intosets of corresponding sections.

The compilation module 214 may present identified media files to users,such that the users may provide contribution data regarding the mediafiles. The contribution data may be used by the crowd source mediaengine 108 to generated condensed media files and compilation files.

In some instances, the compilation module 214 may present a media fileto an electronic device of a user via a web page. The web page mayinclude a user interface that enables the user to select one or moreportions of the media file to retain and one or more portions of themedia file to delete. For example, a media file that is an audiorecording of a meeting may include long pauses between dialogues.Accordingly, the user may manipulate the user interface controls to markthe long pauses for deletion. The compilation module 214 may receive theuser manipulations of the media file as contribution data. Subsequently,the compilation module 214 may modify the media file according to thecontribution data to generate a modified media file. In additionalinstances, the user may use the user interface controls to removeportions of a media file that contains content that may be boring,irrelevant, offensive, or have low value to the user, or retain othercontent that may be of particular importance or interest to generate asummary of the media file.

In other instances, the compilation module 214 may present a media fileto the electronic device of a user via a web page. The web page mayinclude user interface controls that enable the user to submit ratingsfor sections of the media file. Each rating may be a numerical ratingthat is based on a fixed scale (e.g., 3 out of 5 stars). The compilationmodule 214 may collect such ratings from a predetermined number ofusers. Subsequently, the compilation module 214 may generate acompilation file that includes one or more sections of the media file,in which each section has an average rating from the predetermined usersthat exceed a predetermined rating threshold (e.g., at least 3 out of 5stars). The compilation module 214 may subsequently repeat thegeneration of the compilation file after another predetermined number ofusers (e.g., 10 users) have submitted user ratings, and so forth.

In other embodiments, the compilation module 214 may receive binaryvotes for each section of a media file. In other words, a user mayeither vote to keep a section or remove the section from the media file.Accordingly, after a predetermined number of users have submitted votesfor a section, the compilation module 214 may tally up the votes for asection. If a majority of the users voted to keep the section in themedia file, then the compilation module 214 may retain the section inthe resultant compilation file. Conversely, if a majority of the usersvoted to remove the section from the media file, then the compilationmodule 214 may remove the section from the resultant compilation file.The compilation module 214 may subsequently repeat the generation of themodified file after another predetermined number of users (e.g., 10users) have submitted their votes, and so forth.

In additional instances, the compilation module 214 may present multiplerelated media files to the electronic device of the user via a web page.The web page may include user interface controls that enable the user torate sections of the multiple related media files. In one instance, theuser may be presented with multiple related media files that are dividedinto sections by the media division module 212. Some the sections fromthe different related media files may correlate with each other. Forexample, the sections may be discussions of a particular topic from twopresentations of the same lecture. In such an instance, the user may beprompted to assign a rating to each section of the multiple relatedmedia files. The rating may be a numerical rating that is based on afixed scale (e.g., 3 out 5 stars). The compilation module 214 maycollect such ratings from a predetermined number of users. Subsequently,the compilation module 214 may generate a compilation file that includessections from the multiple media files that have the highest ratings, asprovided by the predetermined number of users. The compilation module214 may subsequently repeat the generation of a compilation file afteranother predetermined number of users (e.g., 10 users) have submitteduser ratings, and so forth. Alternatively, the compilation module 214may collect votes on which section from each set of correlated sectionsto keep in the compilation file. Accordingly, the compilation module 214may keep a section from a set of correlated sections for inclusion in acompilation file when the section receives a majority of the votes, andthe remaining sections of the set of correlated sections are discarded.The compilation module 214 may perform such a task for all the sets ofcorrelated sections of the multiple media files. Further, thecompilation module 214 may subsequently repeat the generation of thecompilation file after another predetermined number of users (e.g., 10users) have submitted their votes, and so forth.

In some embodiments, the compilation module 214 may enable users tosubmit other forms of contribution data with respect to the media files.The contribution data may include language translations for spoken wordsin a media file. For example, a user may notice that the spoken words ina particular section of a media file are in a first language while thespoken words in the remaining sections of the media file are in a secondlanguage. In such an instance, the user may use a text input box on auser interface page provided by the compilation module 214 to entertranslation of the spoken words in the particular section from the firstlanguage to the second language. Alternatively or concurrently, thecontribution data may include explanatory material for content in thesections of a media file. For example, a user may notice that aparticular acronym or abbreviation (e.g., SoDo) that is used in asection of a media file may be difficult to understand for a person thatis not familiar with the content of the media file. Thus, the user mayuse a text input box on a user interface page provided by thecompilation module 214 to enter an explanation for the particularacronym or abbreviation (e.g., SoDo represents South of Downtown).Similarly, users may also provide explanations for concepts or ideasthat are discussed in a media file. In another example in which themedia file is an audio file, the explanatory material that iscontributed may include explanation of the context of the audiodescriptions in the audio file. For instance, the audio file may be of alecture in which the presenter is explaining a subject matter usingvisual aids. However, since the media file is an audio file that withoutany visual content, the references of the presenter to the visual aidsmay be unclear. In such an instance, a user with knowledge of theparticular visual aid being discussed and the content of such a visualaid may provide an explanation. Such explanation may be presented astext descriptions, picture in picture video clips, voiceovers that areintegrated with the audio file, and/or other forms of insertions.Alternatively or concurrently, the contribution data submitted withrespect to media files may be in the form of content modification, suchas replacing offensive content (e.g., visually disturbing images orprofanities) in a section of the media file with silence, tones, orsubstitute content (a blank frame, a non-offensive image or speech,etc.). In still other embodiments, the contribution data submitted withrespect to the media files may be in the form of metadata flags thatmark locations offensive content in the sections of the media filesand/or metadata flags that provide content ratings for the sections ofthe media files. For instance, the content ratings may be based onrating system that rates the sections for age appropriateness (e.g., G,PG, PG-13, R, etc.).

In such embodiments, the compilation module 214 may use a user profileof a user to determine whether a modified media file or a compilationfile is to be provided with the supplemental material during generation.The user profile of the user may include the implicit or explicitcharacteristics the user, such as demographic information that isobtained based on the purchases made by the user at one or more onlinemerchants. For example, if the compilation module 214 determines fromthe user profile of a user that the user does not speak a particularlanguage that is used in a section of a media file, the compilationmodule 214 may insert text translation of the content in the section tothe media file. In another example, if the compilation module 214determines from the user profile of the user that the user is unlikelyto be familiar with the content of a compilation file, the compilationmodule 214 may supplement the compilation file with explanatory materialduring the generation of the compilation file.

The compilation module 214 may also take into consideration the implicitand explicit user characteristics of a user to tailor a generatedcompilation file to the user. For example, a media file in the form ofan audio file that includes sections that contain English dialogue andsections that contain French dialogue. However, since the user of therecipient device 118(1) explicitly indicated that the user does notunderstand French, the compilation module 214 may generate a compilationfile that includes only one or more of the sections that contain Englishdialogue. In another example, a media file may include two types ofsections. One type of sections includes audio discussions of a firsttopic (e.g., fashion) and the other type of sections includes audiodiscussion of a second topic (e.g., electronics). In such an example,the compilation module 214 may determine from the purchasing habits ofthe user at one or more online merchants and/or a prior product filedownload history of the user that the user is interested in electronicsbut not fashion. Accordingly, the compilation module 214 may generate aproduct file from the media file that includes one or more sectionspertaining to electronics, but no section that pertains to fashion. Thecompilation module 214 may also obtain user interests in topics andsubjects from mining social media pages or web blog postings of theuser. For example, a user may provide to the compilation module 214identification information of the user at a social media website. Inturn, the compilation module 214 may mine the web page of the user atthe social media website for words or phrases that are indicative of theuser's interest.

While the compilation module 214 is described as being capable ofgenerating compilation files based on user inputted ratings for sectionsof media content, the compilation module 214 is also capable ofleveraging crowd sourced user behavior to generate compilation files. Invarious embodiments, the compilation module 214 may be capable ofmonitoring user consumption behaviors as users view and/or listen tomedia files on electronic devices, such as the electronic devices104(1)-104(N). For example, a content presentation application on anelectronic device may be configured to monitor and report userconsumption behaviors to the compilation module 214 via the network 106.In such embodiments, the compilation module 214 may interpret behaviorsuch as skipping or fast forwarding through content in a section of amedia file as a negative rating for the section (e.g., exclude thesection). Conversely, the compilation module 214 may interpret behaviorsuch as playing a section at normal or slowed speed, replaying thesection, or skipping back to the section as a positive rating for thesection (e.g. retain the section). The compilation module 214 maycollect such negative and positive ratings as users consume a media fileon multiple devices. Accordingly, the compilation module 214 may cut outa particular section to generate a compilation file when a ratio betweennegative ratings and positive ratings for the particular section reach apredetermined ratio threshold (e.g., 10:1. 5:1, 2:1, etc.). However, insome instances, user behavior such as replaying or skipping back to asection of a media file may not indicate that a user has a positiveimpression of the section. Another reason for such behavior may be poorsound and/or visual quality that makes the content of the sectiondifficult to discern. Accordingly, in at least one embodiment, thecompilation module 214 may use an audio quality and/or image qualityassessment algorithm to determine that the replayed or skipped backsection meets a predetermined quality threshold before determining thatthe replay or skip back behavior by a consumer is a positive rating.Alternatively or concurrently, in instances in which metadata flagsprovide content ratings or sections of media files, the compilationmodule 214 may generate product files based on an explicit user contentrating selection for product file compilation. For example, in oneinstance, the compilation module 214 may generate a product file thatincludes sections that are appropriate for viewing by children (e.g.,G-rated sections). However, in another instance, the compilation module214 may generate another product file that includes sections that areappropriate for viewing by both children and adults (e.g., both G-ratedand R-rated sections).

The compilation module 214 may generate media representations of eventsfrom multiple media files. The compilation module 214 may collect geotags and/or temporal tags for media files that are associated with anevent. Accordingly, when a predetermined number or percentage of geotags and/or temporal tags collected for each media file are inagreement, the compilation module 214 may use the resulting location,orientation, and/or temporal data collected for the media files togenerate a stitched media representation of the event. In someinstances, the compilation module 214 may use the metadata flags thatmark the locations of offensive content in the media files, such thatthe compilation module 214 may block the offensive content (e.g.,visually disturbing images or profanities) in a media file from beingpresented with silence, tones, or substitute content (a blank frame, anon-offensive image or speech, etc.). Further, in still otherembodiments, the compilation module 214 may generate stitched mediarepresentations of an event according to content ratings for sections inthe media files that are associated with the event. In such embodiments,a user may provide a content rating selection as a criterion forassembling a stitched media representation, and the compilation module214 may assemble the stitched media representation of the event from oneor more sections of media files that fit the content rating selection.

The distribution module 216 may distribute the product files 116 torecipient devices, such as the recipient device 118(1)-118(N). Theproduct files 116 may include modified media files and compilation filesthat are generated by the compilation module 214. In variousembodiments, the distribution module 216 may distribute a product fileto a recipient device via the network 106 upon receiving a requestoriginating from the recipient device. In some embodiments, thedistribution module 216 may provide a product file to the recipientdevice of a user in exchange for a monetary payment. For example, thedistribution module 216 may charge a fixed amount (e.g., ten cents) forproviding a copy of a particular modified media file.

The media rating module 218 may enable users to submit ratings for theproduct files 116 that are generated by the compilation module 214. Eachrating may be a numerical rating that is based on a fixed scale (e.g., 3out of 5 stars). Accordingly, the media rating module 218 may averagethe ratings that are received for a product file over time to calculatean ongoing rating for the product file. In some instances, the ratingsreceived for the modified media files that are generated from the samemedia file may determine which version of the modified media file may bedelivered to recipient devices. In such instances, the media ratingmodule 218 may select a predetermined number of highest rated modifiedmedia files for distribution to the recipient device upon request.

In other instances, the ratings that are received for the product files116 may impact the ratings of the users that contributed to thegeneration of the product files 116. For example, the rating of a userthat submitted a modified media file may correlate to or is impacted bythe rating that is awarded by one or more reviewers to the modifiedmedia file. The ratings of the product files 116 may also be used todetermine incentives that are awarded to the users that contributed tothe generation of the product files 116.

The user rating module 220 may calculate the user ratings for the usersthat contributed to the generation of the product files 116. Each ratingmay be a numerical rating that is based on a fixed scale (e.g., 3 out of5 stars). In some embodiments, the user rating module 220 may generate arating for a user by averaging the ratings of the multiple modifiedmedia files produced by the user. For example, if a user produced afirst modified media file that received an average rating of 4 stars,and produced a second modified media file that received an averagerating of 3 stars, then the rating of the user may be 3.5 stars. Inother embodiments, the rating of the user may be based on a percentageof times that the ratings assigned by the user to sections of mediafiles agree with average ratings assigned to the sections by multipleusers. For example, a user may assign a rating of 4 stars to a section,and the averages of ratings for the section by a number of other users(e.g., 20 users) may also be 4 stars. In such an instance, the ratingassign by the user to the section may be deemed by the user ratingmodule 220 to be in agreement with the average rating for the section.In other examples, the user rating for a section may also deemed to bein agreement with the average rating for the section when the userrating is within a predetermined deviation (e.g., five percent, tenpercent, etc.) of the average rating for the section. In instances inwhich the compilation module 214 is configured to take binary votes, theuser rating module 220 may count as a rating agreement when a user voteto keep or remove a section conforms to a majority of the votes thatultimately resulted in the section being kept in or removed from acompilation file.

Accordingly, the user rating module 220 may calculate a user rating forthe user based on the percentage of rating agreements. For example, 90%rating agreement and above may produce a rating of 5 stars, 80% ratingagreement and above may product a rating of 4 stars, 70% ratingagreement and above may produce a rating of 3 stars, and so on and soforth. In instances in which a user produces both modified media filesand also rates sections of other media files, the user rating module 220may calculate a rating for the user by averaging the user ratings thatare derived from participation in the generation of one or more modifiedmedia files and derived from participation in the ratings of media filesections. However, other embodiments may include the assignment ofweights to the user ratings from the different participations prior toaveraging. Such weighting may be designed to emphasis rating for oneform of participation over another form of participation. In this way,the rating of a user may be calculated from the quality of the productfiles that are generated from the participation of the user.

In other embodiments, the user rating module 220 may enable users todirectly input user ratings for the users that contributed to thegeneration of the product files 116. Once again, each rating may be anumerical rating that is based on a fixed scale (e.g., 3 out of 5stars). In at least one embodiment, the user rating module 220 mayaverage a directly inputted user rating for a user with one or morecalculated ratings for the user, in which the weighting may be performedbefore the averaging to emphasis one or more of the elemental userratings that are used to derive the final user rating.

The incentive module 222 may provide incentives to users that contributeto the generation of the product files 116. The incentive may benon-monetary. For example, the incentive module 222 may displayattribution information (e.g., name, contact information, etc.) when theuser contributed to the generation of the product file and the rating ofthe user is above a predetermined rating threshold after a predeterminednumber of contribution submissions. The incentive module 222 may alsogrant a user with a user rating that is above a rating threshold withadditional privileges or recognition. For example, the incentive module222 may provide the user with invitations to contribute to thegeneration of product files from high value media files. Such high valuemedia files may be files that deal with complex, technical, and/orspecialized topics. In another example, the incentive module 222 mayassign special status labels to the user based on the number of userratings received by the user that are above one or more particularrating thresholds (e.g., elite reviewer, power reviewer, exceptionalreviewer, and/or so forth). The rating threshold may be a fixedthreshold (e.g., at least 4 of 5 stars) or a relative threshold, such asrating threshold that excludes a predetermined percentage of the lowestrated users (e.g., 90 percentile or above are provide with incentives).

In other embodiments, the incentive module 222 may provide monetaryincentives. In some instances, when the rating of a user exceeds aparticular fixed or relative rating threshold after a predeterminednumber of contribution submissions, the user may be awarded a percentageof the profit that is derived from the sale of a modified media filegenerated by the user. In other instances in which multiple userscontributed ratings of sections that resulted in the compilation module214 generating a compilation file, each user may receive somecompensation for contributing media file or section rating information.Such compensation may be in the form of a coupon, a discount offer,points that are redeemable for merchandise, and/or some other form ofcredit that has some monetary value.

The administration module 224 may enable an administrator to monitor theperformance of the crowd source media engine 108. In variousembodiments, the administrator may interact with the administrationmodule 224 through user interfaces. The user interfaces may include, butare not limited to, combinations of one or more of keypads, keyboards,mouse devices, touch screens, microphones, speech recognition packages,and any other suitable devices or other electronic/software selectioncomponents. Further, the user interfaces may also provide data to theuser via a display. In some instances, the administrator may use theadministration module 224 to review the product files 116 before thedistribution of the product files 116 to the recipient devices118(1)-118(N).

For example, the administrator may review for product files that areoffensive or distort the original content of the media files, such thatcertain product files may be removed from distribution. Theadministration module 224 may also enable the administrator to preventspecific users from contributing to the generation of product files, orrevoke the privilege of specific recipients in obtaining copies of theproduct files 116.

The data store 226 may store the various data that are used by themodules of the crowd source media engine 108. The data may be in theform of metadata that may be manipulated and/or distributed by themodules of the crowd source media engine 108. In various embodiments,the data store may store the media files 112, the product files 116, theidentification information for the media files 112, and the supplementalinformation provided for the media files 112. In some embodiments, theproduct files 116 that are generated may be used by the media collectionmodule 208 as source media files, such that additional product files maybe generated from the one or more products files 116 by the crowd sourcemedia engine 108. Such generation may be based solely on the one or moreproduct files 116, or based on a combination of the one or more productfiles 116 with one or more other media files.

In some instances, the product files 116 that are generated by thecompilation module 214 may include entirely metadata or at least somemetadata, rather may being made up entirely of sections from one or moremedia files. In various embodiments, the metadata may indicate thechronological order of the multiple sections and/or locations of themultiple sections (e.g., a hyperlink, a file directory path, etc.). Forexample, a section of a product file may be located on one of theservers 102, while another section may be located on an entirelydifferent server. The other server may be a server that is operated bythe same organization or a different organization. Accordingly, thedistribution module 216 may perform a distribution of a product file toa recipient device by providing the relevant metadata to the recipientdevice, either alone or in combination with one or more other media filesections. In this way, the recipient device may generate the productfile on-the-fly partially or entirely from the distributed metadata. Forexample, the metadata that is distributed by the distribution module 216may instruct a recipient device to pull a section that is in a firstlanguage (e.g., English) from a first server and a section that is in asecond language (e.g., French) from a second server to assemble into aproduct file.

The data store 226 may further store the various ratings 228 that aregenerated by the media rating module 218 and the user rating module 220,as well as incentives 230 that are awarded to the users. The data store226 may further store tokens that are generated from the spoken words inthe media files.

The modules of the crowd source media engine 108 are described above asusing web pages to display media files and product files to users andreceive contribution data from the users. However, the modules may alsobe configured to use dedicated applications that are installed on theelectronic devices 104(1)-104(N) to achieve the same purpose. Forexample, such dedicated application may render user interface pages onthe electronic devices 104(1)-104(N) under direction from the modules ofthe crowd source media engine 108.

Example User Interfaces

FIG. 3 is a block diagram that illustrates example user interfaces forcrowd source the creation of modified media files from existing mediafiles that include digital content. The user interfaces may be generatedby the compilation module 214. The user interface 302 may enable usersto modify a media file based on the video footage contained in the mediafile. In various embodiments, a selection control 304 of the userinterface 302 may include content selection tools, such as the contentselection tool 306. Each of the content selection tools may enable auser to select portions of the media file to be removed by designating astart and an end of the section to be removed. For example, the contentselection tool 306 may include a start marker 308 and an end marker 310.The start marker 308 may be manipulated to designate the start of asection to be removed, and the end marker 310 may be manipulated todesignate the end of the section to be removed. In such an example, oncethe user has adjusted the start marker 308 and the end marker 310, theuser may activate the remove button 312 to designate the marked sectionfor removal. In this way, the user may use one or more content selectiontools to designate corresponding sections to be removed. The activationof the cancel button 314 may enable the user to undo one or moreselections of sections for removal.

In alternative embodiments, the content selection tools of the selectioncontrol 304 may be used to select sections of the media file to keeprather than sections of media files to be removed. In such embodiments,the remove button 312 may be replaced with a keep button. The user mayuse the keep button to designate sections of the media file that are tobe retained, so that the remaining sections may be removed to form amodified media file.

The user interface 316 may enable users to modify a media file based onthe audio track contained in the media file. In various embodiments, aselection control 318 of the user interface 316 may include contentselection tools, such as the content selection tool 320. Each of thecontent selection tools may enable a user to select portions of themedia file to be removed by designating a start and an end of thesection to be removed. For example, the content selection tool 320 mayinclude a start marker 322 and an end marker 324. The start marker 322may be manipulated to designate the start of a section to be removed,and the end marker 324 may be manipulated to designate the end of thesection to be removed. In such an example, once the user has adjustedthe start marker 322 and the end marker 324, the user may activate theremove button 326 to designate the marked section for removal. In thisway, the user may use one or more content selection tools to designatecorresponding sections to be removed. The activation of the cancelbutton 328 may enable the user to undo one or more selections ofsections for removal.

In alternative embodiments, the content selection tools of the selectioncontrol 318 may be used to select sections of the media file to keeprather than sections of media files to be removed. In such embodiments,the remove button 326 may be replaced with a keep button. The user mayuse the keep button to designate sections of the media file that are tobe retained, so that the remaining sections may be removed to form amodified media file.

In some embodiments, the compilation module 214 may join the sectionsthat are selected for a product file using fading in and/or fade outeffects, such that there is better perceived continuity between thejoined sections. Such effects may be directly added to the product fileby the compilation module 214, or the compilation module 214 may encodemetadata that causes the insertion of such effects into the product fileby another application.

FIG. 4 is a block diagram that illustrates an example user interface 402for crowd sourcing the creation of compilation files from exiting mediafiles. The user interface 402 may be generated by the compilation module214. The user interface 402 may enable a user to rate the sections ofthe multiple media files, such that the compilation module 214 maygenerate a compilation file from the multiple media files. The userinterface 402 may display a set of representations 404(1)-404(N) thatshow media files that are determined to be related by the mediaidentification module 210. Each representation shows the graphicaldepictions of the sections of a corresponding media file. Further, eachrepresentation may be displayed with the relevant media identificationinformation, such as a file identifier, a media file title, date ofcreation, and/or so forth. In various embodiments, the user may select agraphical depiction to activate a media display window. The mediadisplay window may play back audio content, video content, or multimediacontent. Thus, once a user has viewed the content of a section, the usermay assign a rating to a section. The user may assign a rating to eachsection using a corresponding rating assignment control, such as therating assignment control 406. Although the rating assignment controlsare depicted as controls that enable a user to input the number ofstars, the rating assignment controls may enable the input of otherforms of rating data. For example, such rating data may includenumerical values, points, etc. However, the rating data that are enteredfor the sections of the media files using the same rating scale.

For example, as shown in FIG. 4, the user may assign a four star ratingto a section 408 of the media file 404(1), a three star rating tosection 410, a four star rating to the section 412, a two star rating tosection 414, and a three star rating to section 416. In this way, a usermay rate each section of the media files. However, in some instances, amedia file may be missing a section that is present in the remainingrelated media files. For example, the media file 404(3) may be missing asection as a corresponding topic was skipped over in a particularpresentation of the lecture. Accordingly, the user interface 402 mayonly provide a placeholder depiction 418 of the missing section, but nocorresponding rating assignment control. In other instances, a mediafile may include a section that is not present in the remaining relatedmedia files. For example, the media file 404(N) may include a section420 that is not present in the other media files because the sectionpertains to a question answer session that did not occur in the otherpresentations of the lecture.

As described above, the compilation module 214 may select a highestrated section from each group of corresponding sections to generate acompilation file. For example, assume that the compilation module 214has to create a compilation file based on the ratings as shown in FIG.4, the compilation module 214 may select the section 408 of the mediafile 404(1) as “section 1” of the compilation file. Furthermore, thecompilation module 214 may select section 422 of the media file 404(3)as “section 2” of the compilation file and select section 412 of themedia file 404(1) as “section 3” of the compilation file. Additionally,the compilation module 214 may also select section 424 of the media file404(3) as section “4” of the compilation file, section 426 of the mediafile 404(2) as “section 5” of the compilation file, and section 428 ofthe media file 404(N) as section 6″ of the compilation file. Thecompilation module 214 may select section 428 to include in thecompilation file because section 428 has no corresponding sections inthe other media files.

In some embodiment, rather than rating assignment controls, the userinterface 402 may provide voting buttons. The voting buttons may enablea user to vote to keep one section from each set of correlated sectionsof the media files 404(1)-404(N). In other words, users are expected toselect what they consider to be the best “section 1”, the best “section2”, and so on and so forth for inclusion in the final compilation file.

Illustrative Operations

FIGS. 5-11 show illustrative processes 500-1100 that implement crowdsourced processing of media files. Each of the processes 500-1100 isillustrated as a collection of steps in a logical flow diagram, whichrepresents a sequence of operations that can be implemented in hardware,software, or a combination thereof. In the context of software, thesteps represent computer-executable instructions stored on one or morecomputer-readable storage media that, when executed by one or moreprocessors, perform the recited operations. Generally,computer-executable instructions include routines, programs, objects,components, data structures, and the like that perform particularfunctions or implement particular abstract data types. The order inwhich the operations are described is not intended to be construed as alimitation, and any number of the described steps can be combined in anyorder and/or in parallel to implement the process. For discussionpurposes, the processes 500-1100 are described with reference to theenvironment 100 of FIG. 1.

FIG. 5 is a flow diagram of an illustrative process 500 for crowdsourcing modified media files for multiple users and distributing themodified media files. At block 502, the crowd source media engine 108may receive a media file from an electronic device, such as theelectronic device 104(1). In various embodiments, the crowd source mediaengine 108 may receive the media file in response to broadcasting arequest for media files that are related to a topic to multipleelectronic devices. The media file may be transmitted to the crowdsource media engine 108 via the network 106.

At block 504, the crowd source media engine 108 may identify the mediafile for modification. The media file may be identified using at leastone of metadata embedded in the media file, machine learning basedclassification, or crowd sourced classification information. In someembodiments, the modification may involve the removal of dead space(e.g., long pauses) from the media file, the removal of non-relevant oruninteresting material, or the synthesis of important sections of themedia file into a summary version of the media file.

At block 506, the crowd source media engine 108 may generate modifiedmedia files based on contribution data received from multiple electronicdevices, such as the electronic device 104(1)-104(N). The contributiondata may be submitted by users who have listen to and/or viewed themedia files, and marked sections of the media file to be excluded orsections of the media file to be retained.

At block 508, the crowd source media engine 108 may acquire ratings forthe modified media files. In various embodiments, the crowd source mediaengine 108 may distribute the media files to multiple recipient devices,such that the media files may be evaluated by multiple users. In turn,users of the multiple recipient devices may submit ratings for the mediafiles. The crowd source media engine 108 may generate an overall ratingfor a modified media file based on one or more ratings that aresubmitted for the modified media file.

At block 510, the crowd source media engine 108 may calculate a ratingfor each user that contributed a modified media file. The rating for auser may be calculated based on the overall rating of the modified mediafile. In instances in which the user has contributed to the generationof other product files, the crowd source media engine 108 may calculatethe overall rating based on the ratings of all of the product filesgenerated by the user.

FIG. 6 is a flow diagram of an illustrative process 600 for creating acompilation file that is generated based on crowd sourced ratings ofsections in a media file. At block 602, the crowd source media engine108 may receive a media file from an electronic device, such as theelectronic device 104(1). In some embodiments, the crowd source mediaengine 108 may receive the media file in response to broadcasting arequest for media files that are related to a topic to multipleelectronic devices. The media file may be transmitted to the crowdsource media engine 108 via the network 106.

At block 604, the crowd source media engine 108 may identify the mediafile for modification. The media file may be identified using at leastone of metadata embedded in the media file, machine learning basedclassification, or crowd sourced classification information. In someembodiments, the modification may involve the removal of dead space(e.g., long pauses) from the media file, the removal of non-relevant oruninteresting material, or the synthesis of important sections of themedia file into a summary version of the media file.

At block 606, the crowd source media engine 108 parse the media fileinto sections using machine learning partitioning or crowd sourcedpartitioning. Machine-learning partitioning may involve division of themedia file into section according to breaks in audio continuity (e.g.,different speakers or topic) or video continuity (e.g., breaks betweenshots). Crowd sourced partitioning may involve providing the media fileto a plurality of users for analysis, such that the sections of themedia file may be ascertained based on user votes on breaks betweensections.

At block 608, the crowd source media engine 108 may receive crowd sourceuser preferences for the sections from multiple electronic devices. Theuser preferences may be in the form of user ratings for the sections orvotes for sections to retain or exclude. For example, a user may assigna rating to a section that indicates the perceived quality of thesection. Alternatively, the user may vote to retain or exclude a sectionof the media file from a compilation file.

At block 610, the crowd source media engine 108 may generate acompilation file that includes one or more sections of the media filebased on the crowd sourced user preferences for the sections. Forexample, the crowd source media engine 108 may retain a section in agenerated compilation file when the average rating from multiple usersfor a section exceeds a rating threshold. Alternatively, the crowdsource media engine 108 may retain a section when a majority of thevotes received from multiple users for a section is in favor ofretaining the section in the generated compilation file. In otherembodiments, user preferences for a section may be inferred from userbehavior towards the section while the user is listening and/or viewingthe section.

At block 612, the crowd source media engine 108 may distribute thegenerated compilation file to one or more recipient devices. The one ormore recipient devices may belong to users who desire to view thecompilation file. The distribution may be performed in exchange formonetary value.

At block 614, the crowd source media engine 108 may calculate a ratingfor individual users that contributed to the generation of thecompilation file. The rating for a user may be calculated based on thedegree that the rating or the vote of the user agreed with the ratingsor the votes of the other crowd source participants. In instances inwhich a user has contributed to the generation of other product files,the crowd source media engine 108 may calculate the overall rating basedon the ratings of all of the product files generated by the user.

FIG. 7 is a flow diagram of an illustrative process 700 for creating acompilation file that is generated based on crowd source ratings ofsections in multiple media files. At block 702, the crowd source mediaengine 108 may receive a media file from an electronic device, such asthe electronic device 104(1). In some embodiments, the crowd sourcemedia engine 108 may receive the media file in response to broadcastinga request for media files that are related to a topic to multipleelectronic devices. The media file may be transmitted to the crowdsource media engine 108 via the network 106. At block 704, the crowdsource media engine 108 may identify the media file for modification.The media file may be identified using at least one of metadata embeddedin the media file, machine learning based classification, or crowdsourced classification information.

At block 706, the crowd source media engine 108 parse the media fileinto sections using machine learning partitioning or crowd sourcedpartitioning. Machine-learning partitioning may involve division of themedia file into section according to breaks in audio continuity (e.g.,different speakers or topic) or video continuity (e.g., breaks betweenshots). Crowd sourced partitioning may involve provide the media file toa plurality of users for analysis, such that the sections of the mediafile may be ascertained based on user votes on divisions betweensections.

At block 708, the crowd source media engine 108 may group the media filewith one or more similar media files that have partitioned sections. Invarious embodiments, the media file may be grouped with the one or moresimilar media files based on the identification information of the allthe media files. For example, the identification information of all themedia files may indicate that the media files are multimedia recordingsof a particular presentation that is given by a lecturer.

At block 710, the crowd source media engine 108 may receive crowdsourced user preferences for the sections of the multiple media filesfrom multiple electronic devices. The user preferences may be in theform of user ratings for the sections or votes for sections to retain orexclude. For example, a user may assign a rating to section thatindicates the perceived quality of the section. Alternatively, the usermay vote to retain or exclude a section of the media file. In this way,the crowd source media engine 108 may select a section from each set ofcorresponding sections of the multimedia files to include in acompilation file. In other embodiments, user preferences for a sectionmay be inferred from user behavior towards the section while the user islistening and/or viewing the section.

At block 712, the crowd source media engine 108 may generate acompilation file that includes selections selected from the multiplemedia files. In various embodiments, each section that is included inthe compilation file may be a section selected from a set ofcorresponding sections of the multiple media files. The section may be asection that one or more users have voted to retain or a section of thecorresponding sections that received a highest user rating.

At block 714, the crowd source media engine 108 may distribute thegenerate compilation file to one or more recipient devices. The one ormore recipient devices may belong to users who desire to view thecompilation file. The distribution may be performed in exchange formonetary value.

At block 716, the crowd source media engine 108 may calculate a ratingfor individual users that contributed to the generation of thecompilation file. The rating for a user may be calculated based on thedegree that the rating or the vote of the user agreed with the ratingsor the votes of the other crowd source participants. In instances inwhich a user has contributed to the generation of other product files,the crowd source media engine 108 may calculate the overall rating basedon the ratings of all of the product files generated by the user.

FIG. 8 is a flow diagram of an illustrative process 800 for customizinga compilation file based on user characteristics and supplementalmaterial. The process 800 may further describe the block 610 of theprocess 600 or the block 712 of the process 700.

At block 802, the crowd source media engine 108 may obtain one or moreof implicit user characteristics or explicit user characteristics of auser. The implicit user characteristics may be obtained from thepurchase habits of the user at one or more online merchant web sites,social media postings of the user, a prior product file download historyof the user, and/or other sources of information. The explicit usercharacteristics may be information that is specifically provided by theuser to the crowd source media engine 108. For example, the user mayestablish a user profile with the crowd source media engine 108 thatindicates the particular language preferences of the user.

At block 804, the crowd source media engine 108 may generate acompilation file from sections of at least one media file while takinginto account one or more of the implicit user characteristics or theexplicit user characteristics. In various embodiments, the generation ofthe compilation file may proceed as outlined in the block 610 of theprocess 600 or the block 712 of the process 700. However, the implicitor the explicit user characteristics may preclude one or more sectionsfrom being included in the compilation file. For example, the crowdsource media engine 108 may exclude a section having spoken words thatare in a particular language or that pertains to a specific topic fromthe compilation file.

At block 806, the crowd source media engine 108 may supplement thecompilation file with additional material based on a user profile of theuser. The user profile of the user may be derived from the implicit orexplicit information provided by the user, such as demographicinformation that is obtained based on the purchases made by the user atone or more online merchants. For example, if the crowd source mediaengine 108 determines from the user profile of a user that the user doesnot speak a particular language that is used in a section of a mediafile, the crowd source media engine 108 may insert text translation ofthe content in the section to the media file. In another example, if thecrowd source media engine 108 determines from the user profile of theuser that the user is unlikely to be familiar with the content of acompilation file, the crowd source media engine 108 may supplement thecompilation file with explanatory material during the generation of thecompilation file. In alternative embodiments, the crowd source mediaengine 108 may also provide supplement information for a modified mediafiles based on a user profile in a similar manner.

FIG. 9 is a flow diagram of an illustrative process 900 for identifyinga media file based on a comparison of the media file to a known mediafile. At block 902, the crowd source media engine 108 may receive amedia file that includes spoken words. The process 900 may furtherdescribe the block 708 of the process 700. In various embodiments, themedia file may be an audio file or a multimedia file. The media file maybe a file of a particular category. For example, the category may be asubject matter category, an importance category, a source category, orso forth.

At block 904, the crowd source media engine 108 may extract a set oftokens from the spoken words in the media file. The extraction mayinclude the use a speech-to-text algorithm to extract words from theaudio track of the media file. The extracted words are stored as tokens(e.g., nouns, verbs, adjectives, etc.) that may be further processed bythe crowd source media engine 108.

At block 906, the crowd source media engine 108 may identify a matchthreshold for token matching based on the category of the media file. Invarious embodiments, the crowd source media engine 108 may use a list ofmatch thresholds for categories that is stored in the data store 226.For example, a media file that belongs to a higher importance categorymay have a higher match threshold. Conversely, a media file that belongsto a lower importance category may have a lower match threshold.

At block 908, the crowd source media engine 108 may compare the set oftokens that are extracted from the media file with sets of tokens ofknown media files. At decision block 910, the crowd source media engine108 may determine whether the set of extracted tokens match a set oftokens of a known media file by a match threshold. If the crowd sourcemedia engine 108 determines that if the match meets or exceeds the matchthreshold (“yes” at decision block 910), the process 900 may proceed toblock 912.

At block 912, the crowd source media engine 108 may identify the mediafile as being related to the known media file. Accordingly, the crowdsource media engine 108 may assign the identification information fromthe known media file to the media file that is identified. However, ifthe crowd source media engine 108 determines that the match does notmeet the match threshold (“no” at decision block 910), the process 900may proceed to block 914. At block 914, the crowd source media engine108 may determine that the media file is not identifiable.

FIG. 10 is a flow diagram of an illustrative process 1000 for providingan incentive to a user for contributing to the crowd sourced processingof media files. At block 1002, the crowd source media engine 108 maymonitor a user rating of a user that contributes to crowd sourcedprocessing of media files. The contribution of a user may include thegeneration of a modified media file, contribution of ratings or votes tothe generation of a compilation file, contribution of supplementalinformation, and/or so forth.

At block 1004, the crowd source media engine 108 may determine whetherthe user rating of a user meets a rating threshold. In variousembodiments, the user rating may be a rating that is determined after apredetermined number of contribution submissions. The user rating may bea numerical rating that is based on a fixed scale (e.g., 3 out of 5stars). Thus, at decision block 1006, if the crowd source media engine108 determines that the user rating of the user meets a rating threshold(“yes” at decision block 1006), the process 1000 may proceed to block1008.

At block 1008, the crowd source media engine 108 may provide at leastone of monetary or non-monetary incentives to the user. The monetaryincentive may be a flat fee monetary award, or a percentage monetaryaward that is based on proceeds from the sale of a modified media fileor a compilation file. Other monetary awards may include a giftcertificate, a coupon, store credit, or other tokens that may be used tooffset the cost of purchasing goods or services. The non-monetaryincentives may be in the form of recognition, such as an increase inrating on a numerical scale or acknowledgment of contribution increating particular modified media files or compilation files.

However, if the crowd source media engine 108 at decision block 1006determines that the user rating of the does not meet a rating threshold(“no” at decision block 1006), the process 1000 may proceed to return toblock 1002. Upon returning to block 1002, the crowd source media engine108 may continue to monitor the user rating of a user that contributesto the crowd sourced processing of media files.

FIG. 11 is a flow diagram of an illustrative process 1100 for crowdsourcing media files for an event to create a media presentation for theevent. At block 1102, the crowd source media engine 108 may requestmedia files that are related to an event. In various embodiments, therequest may be broadcasted to multiple electronic devices, such as themultiple devices 104(1)-104(N). For example, the users of the multipleelectronic devices may have signed up to receive such a request from thecrowd source media engine 108. In another example, the request may bebroadcasted to electronic devices that belong to a specific group ofusers, such as users that belong to a particular organization or userswho are employed by a particular company. For example, the event may bea company-specific event that is not open to the general public.

At block 1104, the crowd source media engine 108 may receive media filesin response to the request from the multiple electronic devices. Themultiple devices may transmit the media files to the crowd source mediaengine 108 via the network 106. The media files may include audio files,video files, and/or multimedia files.

At block 1106, the crowd source media engine 108 may organize the mediafiles via crowd sourcing. The media files may be taken from differentperspective around the event and/or at different times. In variousembodiments, the crowd source media engine 108 may solicit temporaltagging and/or geo-tagging of the media files from the users of themultiple electronic devices. The temporal tagging and/or geo-tagging ofthe media files may serve to provide organization information for themedia files. In some embodiments, the crowd source media engine 108 mayalso solicit users to identify and/or modify offensive content in thereceived media files. For example, users may mark content that isoffensive with metadata flags.

At block 1108, the crowd source media engine 108 may stitch theorganized media files to generate a media representation of the eventbased on the temporal tags and the geo-tags. The stitched mediarepresentation may simultaneously display multiple views of actionsequences and audio from the event, such that cause and effect betweendifferent occurrences in the event may be understood. Further, if thereare metadata flags that indicate the presence of offensive content, thecrowd source media engine 108 may modify such offensive content. Forexample, the crowd source media engine 108 may block the offensivecontent (e.g., visually disturbing images or profanities) in a mediafile from being presented with silence, tones, or substitute content (ablank frame, a non-offensive image or speech, etc.).

In another example, the crowd source media engine 108 may generate amedia representation of the event according to content ratings forsections in the media files that are associated with the event. In suchan example, a user may provide a content rating preference, and thecompilation module 214 may assemble the media representation of theevent from one or more sections of media files that fit the contentrating preference. In one instance, the crowd source media engine 108may generate a media representation that includes sections that areappropriate for viewing by children (e.g., G-rated sections). However,in another instance, the crowd source media engine 108 may generateanother product file that includes sections that are appropriate forviewing by both children and adults (e.g., both G-rated and R-ratedsections). Accordingly, a user may control the product files that aregenerated by using user content rating selection preferences. In someembodiments, the media representation, similar to other types of productfiles, may include at least some metadata or include entirely ofmetadata that directs an assembly of the media representation frommultiple sources of media files.

At block 1110, the crowd source media engine 108 may distribute thestitched media representation of the event to one or more recipientdevices, such as the recipient devices 118(1)-118(N). The one or morerecipient devices may belong to users who desire to view the mediarepresentation. The distribution may be performed in exchange formonetary value.

In summary, the use of crowd sourced processing of media files may yieldmodified media files and compilation files that provide users with theability to acquire pertinent content in reduced amounts of time. Suchreduction in amount of time may enable users to more efficiently gainknowledge, insight, and/or enjoyment from the media files.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described. Rather,the specific features and acts are disclosed as illustrative forms ofimplementing the claims.

What is claimed is:
 1. One or more non-transitory computer readablemedia storing computer-executable instructions that, when executed,cause one or more processors to perform acts comprising: identifying amedia file that is received from an electronic device; parsing the mediafile into sections using at least one of machine learning partitioninformation or received crowd sourced partition information; groupingthe media file with one or more similar media files to form a group ofmultiple media files having partitioned sections; receiving, frommultiple electronic devices, crowd sourced user preferences for at leastsome of the partitioned sections in the group of multiple media files;generating a product file that includes one or more of the partitionedsections selected from the group of multiple media files based on thecrowd sourced user preferences; distributing the product file to atleast one recipient device; and calculating a user rating for a userthat contributed at least one of the crowd sourced user preferences forgeneration of the product file based at least on one or more productratings of the product file.
 2. The one or more non-transitory computerreadable media of claim 1, wherein the grouping includes matching themedia file to an additional media file based on at least one of metadataincluded in the media file and the additional media file, part of speechtokens in the media file and the additional media file, imagerecognition of images in the media file and the additional file, orcrowd sourced matching information.
 3. The one or more non-transitorycomputer readable media of claim 1, wherein the generating includesselecting a partitioned section for inclusion in the product file inresponse to determining that a rating of the partitioned section ishigher than a rating assigned to at least one other correspondingpartition section or that a majority of a plurality of users voted toinclude the partitioned section in the product file.
 4. The one or morenon-transitory computer readable media of claim 1, wherein thegenerating includes generating metadata that indicates one or moresources for the one or more of the partitioned sections of the productfile.
 5. A method comprising: under control of one or more computingsystems, the one or more computing systems configured with specificexecutable instructions, parsing a media file into sections using atleast one of machine learning partition information or received crowdsourced partition information; grouping the media file with one or moresimilar media files to form a group of multiple media files havingpartitioned sections; generating a product file that includes one ormore of the partitioned sections selected from the group of multiplemedia files based on crowd sourced user preferences; acquiring one ormore product ratings for the product file from at least one otherelectronic device; and calculating a user rating for a user thatprovided at least one of the crowd sourced user preferences forgeneration of the product file based on the one or more product ratingsfor the product file.
 6. The method of claim 5, further comprisingdistributing the product file to the at least one other electronicdevice.
 7. The method of claim 5, further comprising calculating anoverall product rating for the product file as an average of the one ormore product ratings for the product file.
 8. The method of claim 5,wherein the calculating the user rating includes calculating the userrating as an average of a plurality of product ratings for the productfile or as an average of overall product ratings for a plurality ofproduct files generated based on the at least one of the crowd sourceduser preferences provided by the user.
 9. The method of claim 5, furthercomprising providing at least one of a monetary incentive or anon-monetary incentive to the user in response to determining that theuser rating of the user exceeds a rating threshold.
 10. The method ofclaim 5, wherein the generating includes generating the product filethat refrains from including at least one of irrelevant information,offensive content, one or more content sections with a specific contentrating, or dead space from the media file, or generating a product filethat is a summary version of the media file.
 11. One or morenon-transitory computer readable media storing computer-executableinstructions that, when executed, cause one or more processors toperform acts comprising: receiving a media file from an electronicdevice; parsing the media file into multiple sections using machinelearning partition information or received crowd sourced partitioninformation; receiving crowd sourced user preferences for a plurality ofthe multiple sections in the media file; generating a product file thatincludes one or more sections selected from the plurality of themultiple sections based at least on the crowd sourced user preferences;and calculating a user rating for a user that contributed at least oneof the crowd sourced user preferences for generation of the productfile.
 12. The one or more non-transitory computer readable media ofclaim 11, further comprising: grouping the media file with one or moresimilar media files to form a group of multiple media files havingpartitioned sections; receiving crowd sourced user preferences for amultitude of the partitioned sections in the group of multiple mediafiles; and generating an additional product file that includes one ormore sections selected from the group of the multitude of thepartitioned sections based on the crowd sourced user preferences. 13.The one or more non-transitory computer readable media of claim 12,wherein the grouping further comprises: extracting a set of part ofspeech tokens from spoken words in the media file; and identifying themedia file as being related to an additional media file in response todetermining that matching of the set of part of speech tokens to anadditional set of part of speech tokens of the additional media filemeets a match threshold that corresponds to a category of the mediafile.
 14. The one or more non-transitory computer readable media ofclaim 11, further comprising: distributing the product file to at leastone recipient device.
 15. The one or more non-transitory computerreadable media of claim 11, further comprising providing at least one ofa monetary incentive or a non-monetary incentive to the user in responseto determining that the user rating of the user exceeds a ratingthreshold, wherein the rating threshold is a fixed threshold or arelative threshold that is determined based on user ratings ofadditional users that provided contribution information for generationof product files.
 16. The one or more non-transitory computer readablemedia of claim 15, wherein the non-monetary incentive includes at leastone of an attribution to the user associated with the product file or anaward of a status label to the user, and wherein the monetary incentiveincludes at least one of a coupon that is redeemable for goods orservices, a discount offer, one or more points that are redeemable forgoods or services, or one or more credits that have monetary value. 17.The one or more non-transitory computer readable media of claim 11,wherein the receiving includes receiving a user preference to include asection of the media file in response to determining that a userreplayed or skipped back to the section during a playback of the mediafile and an audio quality or image quality of the section is above aquality threshold, or receiving a user preference to exclude the sectionof the media file in response to determining that the user skipped orfast forwarded through the section during the playback.
 18. The one ormore non-transitory computer readable media of claim 11, wherein themachine learning partitioning includes partitioning the media file basedon at least one of metadata embedded in the media file, detected visualdiscontinuities between sections of the media file, or detected audiodiscontinuities between the sections of the media file.
 19. The one ormore non-transitory computer readable media of claim 11, wherein thegenerating includes selecting the one or more sections further based onan explicit user characteristic or an implicit user characteristic of arecipient user.
 20. The one or more non-transitory computer readablemedia of claim 19, wherein the implicit user characteristic of therecipient user is determined based on at least one of a product filedownload history of the recipient user, purchase habit of the recipientuser at one or more merchants, or social media postings of the recipientuser.
 21. The one or more non-transitory computer readable media ofclaim 11, wherein the generating includes supplementing the product filewith additional explanatory material based on a user profile of theuser.
 22. A system, comprising: one or more processors; and memorystoring components that are executable by the one or more processors toperform actions comprising: parsing a media file into sections using atleast one of machine learning partition information or received crowdsourced partition information; grouping the media file with one or moresimilar media files to form a group of multiple media files havingpartitioned sections; receiving, from multiple electronic devices, crowdsourced user preferences for at least some of the partitioned sectionsin the group of multiple media files; generating a product file thatincludes one or more of the partitioned sections selected from the groupof multiple media files based on the crowd sourced user preferences; andcalculating a user rating for a user that contributed at least one ofthe crowd sourced user preferences for generation of the product filebased at least on one or more product ratings of the product file. 23.The system of claim 22, wherein the grouping includes matching the mediafile to an additional media file based on at least one of metadataincluded in the media file and the additional media file, part of speechtokens in the media file and the additional media file, imagerecognition of images in the media file and the additional file, orcrowd sourced matching information.
 24. The system of claim 22, whereinthe generating includes selecting a partitioned section for inclusion inthe product file in response to determining that a rating of thepartitioned section is higher than a rating assigned to at least oneother corresponding partition section or that a majority of a pluralityof users voted to include the partitioned section in the product file.25. The system of claim 22, wherein the generating includes generatingmetadata that indicates one or more sources for the one or more of thepartitioned sections of the product file.